Advances in Machine Learning for Wetland Mapping and Monitoring

Dear Colleagues,

Wetlands are among the most valuable ecosystems on the planet. They provide an abundance of critical services, supporting people and biodiversity. Wetlands buffer against floods, improve water quality, and are globally significant carbon sinks, sequestering huge amounts of carbon in their soils and vegetation. However, these unique habitats are under immense stress from human developments and climate change. The mapping and subsequent monitoring of wetlands is therefore an important endeavor for their conservation and sustainable management.

The application of remote sensing and Earth observation technologies has advanced wetland research by enabling detection and characterization across multiple spatial scales. Despite this progress, significant challenges remain, particularly in capturing the fine-scale complexities and hydrological regimes of wetlands, as well as the need for large and reliable training datasets. Emerging approaches using artificial intelligence (AI) and machine learning, including deep learning and neural networks, are beginning to address these limitations. These technological advancements hold great promise for improving wetland mapping and monitoring.

This Special Issue, in line with Remote Sensing’s emphasis on data and methodological innovations, focuses on recent advances in the remote sensing of wetland ecosystems, with particular attention to developments in AI and machine learning. We welcome studies addressing the following themes:

• Advances in AI and machine learning for wetland mapping and monitoring;
• Deep learning and neural networks for automated wetland detection, classification, and delineation;
• Data and sensor fusion for enhanced wetland characterization;
• Time-series analysis and change detection modeling of wetland dynamics;
• Transferability and scalability of AI models across regions and wetland types;
• Cloud-based machine learning platforms (e.g., Google Earth Engine) for wetland applications;
• Case studies showcasing innovative AI and machine learning applications in wetland remote sensing.

Dr. Michael Allan Merchant
Guest Editor

Manuscript Submission Information

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